A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks

Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making t...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2023-09, Vol.12 (18), p.3748
Main Authors: Lv, Zhongming, Tong, Xiangrong
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Decision support systems
Decomposition
Deep learning
Electronic data processing
Feedback
Gaming machines
Graph neural networks
Graph theory
Graphical representations
Neural networks
Recommender systems
Regression analysis
Reinforcement learning (Machine learning)
Social networks
Sparsity
Training
User behavior
User experience
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making them isolated from each other, missing some useful infomation for recommendation. In addition, the traditional non-end-to-end learning series takes a long time and accumulates errors. During the model training process, the results of each task can easily affect the next calculation, thus affecting the entire training effect. Aiming at the above problems, this paper proposes a Reinforcement Learning List Recommendation Model Fused with a Graph Neural Network, GNLR. The goal of this model is to maximize the recommendation effect while ensuring that the list recommendation system accurately analyzes user preferences to improve user experience. To this end, firstly, we use an user–item bipartite graph and Graph Neural Network to aggregate neighborhood information for users and items to generate graph structured representation; secondly, we adopt an attention mechanism to assign corresponding weights to neighborhood information to reduce the influence of noise nodes in heterogeneous information networks; finally, we alleviate the problems of traditional non-end-to-end methods through end-to-end training methods. The experimental results show that the method proposed in this paper can alleviate the above problems, and the recommendation hit rate and accuracy rate increase by about 10%.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12183748